With each passing day, millions of people use applications that interact with machines through various media including voice. An efficient Speech Recognition (SR) program has the ability to transform enterprises for business gains, but the limited scope and accuracy of such software in the past has been an Achilles heel for developers and scientists.
Unreliability was often the major talking point in the past when it came to Speech Recognition tools. However, the proactive use of technologies like Artificial intelligence (AI) and Machine learning (ML) have together changed the face of Speech Recognition technology. Such has been the impact of AI and ML on Speech Recognition that today they are the new ‘normals’, ensuring Speech Recognition is accurate and enables multiple dimensions in analytics.
Impact of AI and ML on Speech Recognition technology
Technology giant IBM has recently announced that its Speech Recognition software managed to achieve a 5.5% word error rate. This is an improvement over the 5.9% attained by Microsoft’s Artificial Intelligence and Research group in October 2016. Such AI- and ML- induced breakthroughs in Speech Recognition technology means that it is only a matter of time when software may recognize speech as accurately as a professional human transcriber. Reaching human-level performance in Speech Recognition is likely to have massive implications for the future of AI and Speech Recognition industry as a whole.
Role of AI and ML in overcoming Speech Recognition challenges
Before analyzing the role of AI and ML in Speech Recognition tools, it is significant to understand the impact of both technologies. Many times, the terms ML and AI are interchangeably used although both have their own distinct role to play in ensuring Speech Recognition reaches the next level of perfection.
AI is best understood as intelligence that is exhibited by the machine or device itself by perceiving its environment and surroundings. Machine Learning, on the other hand, is the ability of the computer or machine to learn trends without being excessively programmed. In Speech Recognition tools, Machine Learning has helped transform Natural Language Processing (NLP) while AI tracks any changes in speech modulation, allowing for prediction and analysis of emotions, accents, and behavior patterns by decoding changes in voice.
Smarter Speech Recognition is the future
Detecting emotions in a user interacting with the Speech Recognition software or tracking keywords for data analytics are hallmark capabilities of AI capabilities. Similarly, dialect tracking allowing Speech Recognition tools to adopt a self-learning mechanism and track associated KPIs is the capability ML brings to Speech Recognition tools.
Conclusion: With the emergence of AI and ML capabilities, the future roadmap of Speech Recognition technology is paved with smarter solutions offering high accuracy.
To further understand the importance of Artificial Intelligence and Machine Learning in Speech Recognition tech and its future applications, read the Whitepaper The Relevance of Artificial Intelligence and Machine Learning in Speech Recognition
